Self-Adaptive Measurement Matrix Design and Channel Estimation in Time-Varying Hybrid MmWave Massive MIMO-OFDM Systems

Channel estimation in hybrid massive multiple input multiple output (MIMO) orthogonal frequency division multiplexing (OFDM) systems is challenging as only low-dimensional channel measurement can be obtained at the receiver while the true channel is high-dimensional. In the compressed sensing (CS) based channel estimation algorithms, a well-designed measurement matrix will contribute to better estimation performance. Therefore, a temporal correlation-based self-adaptive measurement matrix design (TC-SAMMD) method is proposed, and the corresponding frame structure and channel estimation technique are developed in this paper. Specifically, pilots are transmitted twice during each block, and the process of channel estimation contains three steps: time-varying channel estimation by Kalman filtering, self-adaptive measurement matrix design, and two pilots-based channel estimation via the simultaneous orthogonal matching pursuit (SOMP) algorithm. Simulation results demonstrate that the proposed TC-SAMMD method outperforms conventional measurement matrix design approaches in terms of the channel estimation error, thanks to the "adaptiveness" to the channel's characteristics in consecutive blocks. Besides, it is shown that the TC-SAMMD strategy with a full-ranged innovation noise can effectively mitigate the performance degradation caused by the angle shifts.